{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e1b280ab-b61f-4d1a-bf7e-44e5f9ed3a5c",
   "metadata": {
    "id": "e1b280ab-b61f-4d1a-bf7e-44e5f9ed3a5c"
   },
   "source": [
    "<table style=\"width:100%\">\n",
    "<tr>\n",
    "<td style=\"vertical-align:middle; text-align:left;\">\n",
    "<font size=\"2\">\n",
    "Supplementary code for the <a href=\"http://mng.bz/orYv\">Build a Large Language Model From Scratch</a> book by <a href=\"https://sebastianraschka.com\">Sebastian Raschka</a><br>\n",
    "<br>Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n",
    "</font>\n",
    "</td>\n",
    "<td style=\"vertical-align:middle; text-align:left;\">\n",
    "<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>\n",
    "</td>\n",
    "</tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efde77f2-6af3-4781-8597-89ecd3f41a52",
   "metadata": {
    "id": "efde77f2-6af3-4781-8597-89ecd3f41a52"
   },
   "source": [
    "# Llama 3.2 From Scratch (A Standalone Notebook)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55cdef4d-de59-4a65-89f9-fa2a8ef3471d",
   "metadata": {
    "id": "55cdef4d-de59-4a65-89f9-fa2a8ef3471d"
   },
   "source": [
    "**Note: This notebook is an alternative to the [standalone-llama32.ipynb](standalone-llama32.ipynb) notebook but optimized for memory efficiency by using a global mask, cos, and sin. On an A100, based on a 8192 context length, this only uses 3.1 GB (vs 7.07 GB) VRAM.** \n",
    "\n",
    "\n",
    "- This notebook is purposefully minimal and focuses on the code to implement the Llama 3.2 1B and 3B LLMs\n",
    "- For a step-by-step guide that explains the individual components and the relationship between GPT, Llama 2, and Llama 3, please see the following companion notebooks:\n",
    "  - [Converting a From-Scratch GPT Architecture to Llama 2](converting-gpt-to-llama2.ipynb)\n",
    "  - [Converting Llama 2 to Llama 3.2 From Scratch](converting-llama2-to-llama3.ipynb)\n",
    "  \n",
    "\n",
    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gpt-to-llama/llama32.webp\" width=\"700px\">\n",
    "  \n",
    "  \n",
    "- About the code:\n",
    "  - all code is my own code, mapping the Llama 3 architecture onto the model code implemented in my [Build A Large Language Model (From Scratch)](http://mng.bz/orYv) book; the code is released under a permissive open-source Apache 2.0 license (see [LICENSE.txt](https://github.com/rasbt/LLMs-from-scratch/blob/main/LICENSE.txt))\n",
    "  - the tokenizer code is inspired by the original [Llama 3 tokenizer code](https://github.com/meta-llama/llama3/blob/main/llama/tokenizer.py), which Meta AI used to extend the Tiktoken GPT-4 tokenizer\n",
    "  - the RoPE rescaling section is inspired by the [_compute_llama3_parameters function](https://github.com/huggingface/transformers/blob/5c1027bf09717f664b579e01cbb8ec3ef5aeb140/src/transformers/modeling_rope_utils.py#L329-L347) in the `transformers` library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7c201adb-747e-437b-9a62-442802941e01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install -r https://raw.githubusercontent.com/rasbt/LLMs-from-scratch/refs/heads/main/ch05/07_gpt_to_llama/requirements-extra.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd1b65a8-4301-444a-bd7c-a6f2bd1df9df",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dd1b65a8-4301-444a-bd7c-a6f2bd1df9df",
    "outputId": "4f762354-e0a3-4cc2-e5d4-e61a227a202c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blobfile version: 3.0.0\n",
      "huggingface_hub version: 0.29.3\n",
      "tiktoken version: 0.9.0\n",
      "torch version: 2.6.0\n"
     ]
    }
   ],
   "source": [
    "from importlib.metadata import version\n",
    "\n",
    "pkgs = [\n",
    "    \"blobfile\",         # to download pretrained weights\n",
    "    \"huggingface_hub\",  # to download pretrained weights\n",
    "    \"tiktoken\",         # to implement the tokenizer\n",
    "    \"torch\",            # to implement the model\n",
    "]\n",
    "for p in pkgs:\n",
    "    print(f\"{p} version: {version(p)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "653410a6-dd2b-4eb2-a722-23d9782e726d",
   "metadata": {
    "id": "653410a6-dd2b-4eb2-a722-23d9782e726d"
   },
   "source": [
    "&nbsp;\n",
    "# 1. Architecture code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "82076c21-9331-4dcd-b017-42b046cf1a60",
   "metadata": {
    "id": "82076c21-9331-4dcd-b017-42b046cf1a60"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "\n",
    "class FeedForward(nn.Module):\n",
    "    def __init__(self, cfg):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(cfg[\"emb_dim\"], cfg[\"hidden_dim\"], dtype=cfg[\"dtype\"], bias=False)\n",
    "        self.fc2 = nn.Linear(cfg[\"emb_dim\"], cfg[\"hidden_dim\"], dtype=cfg[\"dtype\"], bias=False)\n",
    "        self.fc3 = nn.Linear(cfg[\"hidden_dim\"], cfg[\"emb_dim\"], dtype=cfg[\"dtype\"], bias=False)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x_fc1 = self.fc1(x)\n",
    "        x_fc2 = self.fc2(x)\n",
    "        x = nn.functional.silu(x_fc1) * x_fc2\n",
    "        return self.fc3(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4b9a346f-5826-4083-9162-abd56afc03f0",
   "metadata": {
    "id": "4b9a346f-5826-4083-9162-abd56afc03f0"
   },
   "outputs": [],
   "source": [
    "def compute_rope_params(head_dim, theta_base=10_000, context_length=4096, freq_config=None, dtype=torch.float32):\n",
    "    assert head_dim % 2 == 0, \"Embedding dimension must be even\"\n",
    "\n",
    "    # Compute the inverse frequencies\n",
    "    inv_freq = 1.0 / (theta_base ** (torch.arange(0, head_dim, 2, dtype=dtype)[: (head_dim // 2)].float() / head_dim))\n",
    "\n",
    "    # Frequency adjustments\n",
    "    if freq_config is not None:\n",
    "        low_freq_wavelen = freq_config[\"original_context_length\"] / freq_config[\"low_freq_factor\"]\n",
    "        high_freq_wavelen = freq_config[\"original_context_length\"] / freq_config[\"high_freq_factor\"]\n",
    "\n",
    "        wavelen = 2 * torch.pi / inv_freq\n",
    "\n",
    "        inv_freq_llama = torch.where(\n",
    "            wavelen > low_freq_wavelen, inv_freq / freq_config[\"factor\"], inv_freq\n",
    "        )\n",
    "\n",
    "        smooth_factor = (freq_config[\"original_context_length\"] / wavelen - freq_config[\"low_freq_factor\"]) / (\n",
    "            freq_config[\"high_freq_factor\"] - freq_config[\"low_freq_factor\"]\n",
    "        )\n",
    "\n",
    "        smoothed_inv_freq = (\n",
    "            (1 - smooth_factor) * (inv_freq / freq_config[\"factor\"]) + smooth_factor * inv_freq\n",
    "        )\n",
    "\n",
    "        is_medium_freq = (wavelen <= low_freq_wavelen) & (wavelen >= high_freq_wavelen)\n",
    "        inv_freq_llama = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama)\n",
    "        inv_freq = inv_freq_llama\n",
    "\n",
    "    # Generate position indices\n",
    "    positions = torch.arange(context_length, dtype=dtype)\n",
    "\n",
    "    # Compute the angles\n",
    "    angles = positions[:, None] * inv_freq[None, :]  # Shape: (context_length, head_dim // 2)\n",
    "\n",
    "    # Expand angles to match the head_dim\n",
    "    angles = torch.cat([angles, angles], dim=1)  # Shape: (context_length, head_dim)\n",
    "\n",
    "    # Precompute sine and cosine\n",
    "    cos = torch.cos(angles)\n",
    "    sin = torch.sin(angles)\n",
    "\n",
    "    return cos, sin\n",
    "\n",
    "\n",
    "def apply_rope(x, cos, sin):\n",
    "    # x: (batch_size, num_heads, seq_len, head_dim)\n",
    "    batch_size, num_heads, seq_len, head_dim = x.shape\n",
    "    assert head_dim % 2 == 0, \"Head dimension must be even\"\n",
    "\n",
    "    # Split x into first half and second half\n",
    "    x1 = x[..., : head_dim // 2]  # First half\n",
    "    x2 = x[..., head_dim // 2 :]  # Second half\n",
    "\n",
    "    # Adjust sin and cos shapes\n",
    "    cos = cos[:seq_len, :].unsqueeze(0).unsqueeze(0)  # Shape: (1, 1, seq_len, head_dim)\n",
    "    sin = sin[:seq_len, :].unsqueeze(0).unsqueeze(0)\n",
    "\n",
    "    # Apply the rotary transformation\n",
    "    rotated = torch.cat((-x2, x1), dim=-1)\n",
    "    x_rotated = (x * cos) + (rotated * sin)\n",
    "\n",
    "    # It's ok to use lower-precision after applying cos and sin rotation\n",
    "    return x_rotated.to(dtype=x.dtype)\n",
    "\n",
    "\n",
    "def rescale_theta(theta_old, context_length_old, context_length_new):\n",
    "    scaling_factor = context_length_new / context_length_old\n",
    "    theta_new = theta_old * scaling_factor\n",
    "    return theta_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e8169ab5-f976-4222-a2e1-eb1cabf267cb",
   "metadata": {
    "id": "e8169ab5-f976-4222-a2e1-eb1cabf267cb"
   },
   "outputs": [],
   "source": [
    "class GroupedQueryAttention(nn.Module):\n",
    "    def __init__(\n",
    "            self, d_in, d_out, num_heads,\n",
    "            num_kv_groups,\n",
    "            dtype=None\n",
    "        ):\n",
    "        super().__init__()\n",
    "        assert d_out % num_heads == 0, \"d_out must be divisible by num_heads\"\n",
    "        assert num_heads % num_kv_groups == 0, \"num_heads must be divisible by num_kv_groups\"\n",
    "\n",
    "        self.d_out = d_out\n",
    "        self.num_heads = num_heads\n",
    "        self.head_dim = d_out // num_heads\n",
    "\n",
    "        self.W_key = nn.Linear(d_in, num_kv_groups * self.head_dim, bias=False, dtype=dtype)\n",
    "        self.W_value = nn.Linear(d_in, num_kv_groups * self.head_dim, bias=False, dtype=dtype)\n",
    "        self.num_kv_groups = num_kv_groups\n",
    "        self.group_size = num_heads // num_kv_groups\n",
    "\n",
    "        self.W_query = nn.Linear(d_in, d_out, bias=False, dtype=dtype)\n",
    "        self.out_proj = nn.Linear(d_out, d_out, bias=False, dtype=dtype)\n",
    "\n",
    "    def forward(self, x, mask, cos, sin):\n",
    "        b, num_tokens, d_in = x.shape\n",
    "\n",
    "        queries = self.W_query(x)  # Shape: (b, num_tokens, d_out)\n",
    "        keys = self.W_key(x)  # Shape: (b, num_tokens, num_kv_groups * head_dim)\n",
    "        values = self.W_value(x)  # Shape: (b, num_tokens, num_kv_groups * head_dim)\n",
    "\n",
    "        # Reshape queries, keys, and values\n",
    "        queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)\n",
    "        keys = keys.view(b, num_tokens, self.num_kv_groups, self.head_dim)\n",
    "        values = values.view(b, num_tokens, self.num_kv_groups, self.head_dim)\n",
    "\n",
    "        # Transpose keys, values, and queries\n",
    "        keys = keys.transpose(1, 2)  # Shape: (b, num_heads, num_tokens, head_dim)\n",
    "        values = values.transpose(1, 2)  # Shape: (b, num_heads, num_tokens, head_dim)\n",
    "        queries = queries.transpose(1, 2)  # Shape: (b, num_query_groups, num_tokens, head_dim)\n",
    "\n",
    "        # Apply RoPE\n",
    "        keys = apply_rope(keys, cos, sin)\n",
    "        queries = apply_rope(queries, cos, sin)\n",
    "\n",
    "        # Expand keys and values to match the number of heads\n",
    "        # Shape: (b, num_heads, num_tokens, head_dim)\n",
    "        keys = keys.repeat_interleave(self.group_size, dim=1)  # Shape: (b, num_heads, num_tokens, head_dim)\n",
    "        values = values.repeat_interleave(self.group_size, dim=1)  # Shape: (b, num_heads, num_tokens, head_dim)\n",
    "        # For example, before repeat_interleave along dim=1 (query groups):\n",
    "        #   [K1, K2]\n",
    "        # After repeat_interleave (each query group is repeated group_size times):\n",
    "        #   [K1, K1, K2, K2]\n",
    "        # If we used regular repeat instead of repeat_interleave, we'd get:\n",
    "        #   [K1, K2, K1, K2]\n",
    "\n",
    "        # Compute scaled dot-product attention (aka self-attention) with a causal mask\n",
    "        # Shape: (b, num_heads, num_tokens, num_tokens)\n",
    "        attn_scores = queries @ keys.transpose(2, 3)  # Dot product for each head\n",
    "\n",
    "        # Use the mask to fill attention scores\n",
    "        attn_scores = attn_scores.masked_fill(mask[:num_tokens, :num_tokens], -torch.inf)\n",
    "\n",
    "        attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)\n",
    "        assert keys.shape[-1] == self.head_dim\n",
    "\n",
    "        # Shape: (b, num_tokens, num_heads, head_dim)\n",
    "        context_vec = (attn_weights @ values).transpose(1, 2)\n",
    "\n",
    "        # Combine heads, where self.d_out = self.num_heads * self.head_dim\n",
    "        context_vec = context_vec.reshape(b, num_tokens, self.d_out)\n",
    "        context_vec = self.out_proj(context_vec)  # optional projection\n",
    "\n",
    "        return context_vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "457cb2f8-50c1-4045-8a74-f181bfb5fea9",
   "metadata": {
    "id": "457cb2f8-50c1-4045-8a74-f181bfb5fea9"
   },
   "outputs": [],
   "source": [
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, cfg):\n",
    "        super().__init__()\n",
    "        self.att = GroupedQueryAttention(\n",
    "            d_in=cfg[\"emb_dim\"],\n",
    "            d_out=cfg[\"emb_dim\"],\n",
    "            num_heads=cfg[\"n_heads\"],\n",
    "            num_kv_groups=cfg[\"n_kv_groups\"],\n",
    "            dtype=cfg[\"dtype\"]\n",
    "        )\n",
    "        self.ff = FeedForward(cfg)\n",
    "        self.norm1 = nn.RMSNorm(cfg[\"emb_dim\"], eps=1e-5, dtype=cfg[\"dtype\"])\n",
    "        self.norm2 = nn.RMSNorm(cfg[\"emb_dim\"], eps=1e-5, dtype=cfg[\"dtype\"])\n",
    "\n",
    "    def forward(self, x, mask, cos, sin):\n",
    "        # Shortcut connection for attention block\n",
    "        shortcut = x\n",
    "        x = self.norm1(x)\n",
    "        x = self.att(x, mask, cos, sin)  # Shape [batch_size, num_tokens, emb_size]\n",
    "        x = x + shortcut  # Add the original input back\n",
    "\n",
    "        # Shortcut connection for feed-forward block\n",
    "        shortcut = x\n",
    "        x = self.norm2(x)\n",
    "        x = self.ff(x)\n",
    "        x = x + shortcut  # Add the original input back\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e88de3e3-9f07-42cc-816b-28dbd46e96c4",
   "metadata": {
    "id": "e88de3e3-9f07-42cc-816b-28dbd46e96c4"
   },
   "outputs": [],
   "source": [
    "class Llama3Model(nn.Module):\n",
    "    def __init__(self, cfg):\n",
    "        super().__init__()\n",
    "\n",
    "        # Main model parameters\n",
    "        self.tok_emb = nn.Embedding(cfg[\"vocab_size\"], cfg[\"emb_dim\"], dtype=cfg[\"dtype\"])\n",
    "\n",
    "        self.trf_blocks = nn.ModuleList(  # ModuleList since Sequential can only accept one input, and we need `x, mask, cos, sin`\n",
    "            [TransformerBlock(cfg) for _ in range(cfg[\"n_layers\"])]\n",
    "        )\n",
    "\n",
    "        self.final_norm = nn.RMSNorm(cfg[\"emb_dim\"], eps=1e-5, dtype=cfg[\"dtype\"])\n",
    "        self.out_head = nn.Linear(cfg[\"emb_dim\"], cfg[\"vocab_size\"], bias=False, dtype=cfg[\"dtype\"])\n",
    "\n",
    "        # Reusuable utilities\n",
    "        self.register_buffer(\n",
    "            \"mask\", torch.triu(torch.ones(cfg[\"context_length\"], cfg[\"context_length\"]), diagonal=1).bool(),\n",
    "            persistent=False\n",
    "        )\n",
    "        cfg[\"rope_base\"] = rescale_theta(\n",
    "                        cfg[\"rope_base\"],\n",
    "                        cfg[\"orig_context_length\"],\n",
    "                        cfg[\"context_length\"]\n",
    "                    )\n",
    "        cos, sin = compute_rope_params(\n",
    "            head_dim=cfg[\"emb_dim\"] // cfg[\"n_heads\"],\n",
    "            theta_base=cfg[\"rope_base\"],\n",
    "            context_length=cfg[\"context_length\"],\n",
    "            freq_config=cfg[\"rope_freq\"]\n",
    "        )\n",
    "        self.register_buffer(\"cos\", cos)\n",
    "        self.register_buffer(\"sin\", sin)\n",
    "        self.cfg = cfg\n",
    "\n",
    "\n",
    "    def forward(self, in_idx):\n",
    "        # Forward pass\n",
    "        tok_embeds = self.tok_emb(in_idx)\n",
    "        x = tok_embeds\n",
    "        \n",
    "        for block in self.trf_blocks:\n",
    "            x = block(x, self.mask, self.cos, self.sin)\n",
    "        x = self.final_norm(x)\n",
    "        logits = self.out_head(x.to(self.cfg[\"dtype\"]))\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be2d201f-74ad-4d63-ab9c-601b00674a48",
   "metadata": {
    "id": "be2d201f-74ad-4d63-ab9c-601b00674a48"
   },
   "source": [
    "&nbsp;\n",
    "# 2. Initialize model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23dea40c-fe20-4a75-be25-d6fce5863c01",
   "metadata": {
    "id": "23dea40c-fe20-4a75-be25-d6fce5863c01"
   },
   "source": [
    "- The remainder of this notebook uses the Llama 3.2 1B model; to use the 3B model variant, just uncomment the second configuration file in the following code cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "caa142fa-b375-4e78-b392-2072ced666f3",
   "metadata": {
    "id": "caa142fa-b375-4e78-b392-2072ced666f3"
   },
   "outputs": [],
   "source": [
    "# Llama 3.2 1B\n",
    "\n",
    "LLAMA32_CONFIG = {\n",
    "    \"vocab_size\": 128_256,           # Vocabulary size\n",
    "    \"context_length\": 8192,          # Maximum context length to use (reduced to save memory)\n",
    "    \"orig_context_length\": 131_072,  # Context length that was used to train the model\n",
    "    \"emb_dim\": 2048,                 # Embedding dimension\n",
    "    \"n_heads\": 32,                   # Number of attention heads\n",
    "    \"n_layers\": 16,                  # Number of layers\n",
    "    \"hidden_dim\": 8192,              # Size of the intermediate dimension in FeedForward\n",
    "    \"n_kv_groups\": 8,                # Key-Value groups for grouped-query attention\n",
    "    \"rope_base\": 500_000.0,          # The base in RoPE's \"theta\"\n",
    "    \"dtype\": torch.bfloat16,         # Lower-precision dtype to reduce memory usage\n",
    "    \"rope_freq\": {                   # RoPE frequency scaling\n",
    "        \"factor\": 32.0,\n",
    "        \"low_freq_factor\": 1.0,\n",
    "        \"high_freq_factor\": 4.0,\n",
    "        \"original_context_length\": 8192,\n",
    "    }\n",
    "}\n",
    "\n",
    "# Llama 3.2 3B\n",
    "\n",
    "# LLAMA32_CONFIG = {\n",
    "#     \"vocab_size\": 128_256,           # Vocabulary size\n",
    "#     \"context_length\": 8192,          # Maximum context length to use (reduced to save memory)\n",
    "#     \"orig_context_length\": 131_072,  # Context length that was used to train the model\n",
    "#     \"emb_dim\": 3072,                 # Embedding dimension\n",
    "#     \"n_heads\": 24,                   # Number of attention heads\n",
    "#     \"n_layers\": 28,                  # Number of layers\n",
    "#     \"hidden_dim\": 8192,              # Size of the intermediate dimension in FeedForward\n",
    "#     \"n_kv_groups\": 8,                # Key-Value groups for grouped-query attention\n",
    "#     \"rope_base\": 500_000.0,          # The base in RoPE's \"theta\"\n",
    "#     \"dtype\": torch.bfloat16,         # Lower-precision dtype to reduce memory usage\n",
    "#     \"rope_freq\": {                   # RoPE frequency scaling\n",
    "#         \"factor\": 32.0,\n",
    "#         \"low_freq_factor\": 1.0,\n",
    "#         \"high_freq_factor\": 4.0,\n",
    "#         \"original_context_length\": 8192,\n",
    "#     }\n",
    "# }\n",
    "\n",
    "LLAMA_SIZE_STR = \"1B\" if LLAMA32_CONFIG[\"emb_dim\"] == 2048 else \"3B\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "156253fe-aacd-4da2-8f13-705f05c4b11e",
   "metadata": {
    "id": "156253fe-aacd-4da2-8f13-705f05c4b11e"
   },
   "outputs": [],
   "source": [
    "model = Llama3Model(LLAMA32_CONFIG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19de6c2c-83ce-456d-8be9-6ec415fe9eb1",
   "metadata": {
    "id": "19de6c2c-83ce-456d-8be9-6ec415fe9eb1"
   },
   "source": [
    "- The following is expected to print True to confirm buffers are reused instead of being (wastefully) recreated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "364e76ca-52f8-4fa5-af37-c4069f9694bc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "364e76ca-52f8-4fa5-af37-c4069f9694bc",
    "outputId": "00d7e983-262e-4c65-f322-f4d999311988"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of parameters: 1,498,482,688\n",
      "\n",
      "Total number of unique parameters: 1,235,814,400\n"
     ]
    }
   ],
   "source": [
    "total_params = sum(p.numel() for p in model.parameters())\n",
    "print(f\"Total number of parameters: {total_params:,}\")\n",
    "\n",
    "# Account for weight tying\n",
    "total_params_normalized = total_params - model.tok_emb.weight.numel()\n",
    "print(f\"\\nTotal number of unique parameters: {total_params_normalized:,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fd5efb03-5a07-46e8-8607-93ed47549d2b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fd5efb03-5a07-46e8-8607-93ed47549d2b",
    "outputId": "65c1a95e-b502-4150-9e2e-da619d9053d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float32 (PyTorch default): 11.42 GB\n",
      "bfloat16: 5.71 GB\n"
     ]
    }
   ],
   "source": [
    "def model_memory_size(model, input_dtype=torch.float32):\n",
    "    total_params = 0\n",
    "    total_grads = 0\n",
    "    for param in model.parameters():\n",
    "        # Calculate total number of elements per parameter\n",
    "        param_size = param.numel()\n",
    "        total_params += param_size\n",
    "        # Check if gradients are stored for this parameter\n",
    "        if param.requires_grad:\n",
    "            total_grads += param_size\n",
    "\n",
    "    # Calculate buffer size (non-parameters that require memory)\n",
    "    total_buffers = sum(buf.numel() for buf in model.buffers())\n",
    "\n",
    "    # Size in bytes = (Number of elements) * (Size of each element in bytes)\n",
    "    # We assume parameters and gradients are stored in the same type as input dtype\n",
    "    element_size = torch.tensor(0, dtype=input_dtype).element_size()\n",
    "    total_memory_bytes = (total_params + total_grads + total_buffers) * element_size\n",
    "\n",
    "    # Convert bytes to gigabytes\n",
    "    total_memory_gb = total_memory_bytes / (1024**3)\n",
    "\n",
    "    return total_memory_gb\n",
    "\n",
    "print(f\"float32 (PyTorch default): {model_memory_size(model, input_dtype=torch.float32):.2f} GB\")\n",
    "print(f\"bfloat16: {model_memory_size(model, input_dtype=torch.bfloat16):.2f} GB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "31f12baf-f79b-499f-85c0-51328a6a20f5",
   "metadata": {
    "id": "31f12baf-f79b-499f-85c0-51328a6a20f5"
   },
   "outputs": [],
   "source": [
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")\n",
    "elif torch.backends.mps.is_available():\n",
    "    device = torch.device(\"mps\")\n",
    "else:\n",
    "    device = torch.device(\"cpu\")\n",
    "\n",
    "model.to(device);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78e091e1-afa8-4d23-9aea-cced86181bfd",
   "metadata": {
    "id": "78e091e1-afa8-4d23-9aea-cced86181bfd"
   },
   "source": [
    "&nbsp;\n",
    "# 3. Load tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9482b01c-49f9-48e4-ab2c-4a4c75240e77",
   "metadata": {
    "id": "9482b01c-49f9-48e4-ab2c-4a4c75240e77"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "import tiktoken\n",
    "from tiktoken.load import load_tiktoken_bpe\n",
    "\n",
    "\n",
    "class Tokenizer:\n",
    "    def __init__(self, model_path):\n",
    "        assert os.path.isfile(model_path), f\"Model file {model_path} not found\"\n",
    "        mergeable_ranks = load_tiktoken_bpe(model_path)\n",
    "\n",
    "        self.special_tokens = {\n",
    "            \"<|begin_of_text|>\": 128000,\n",
    "            \"<|end_of_text|>\": 128001,\n",
    "            \"<|start_header_id|>\": 128006,\n",
    "            \"<|end_header_id|>\": 128007,\n",
    "            \"<|eot_id|>\": 128009,\n",
    "        }\n",
    "        self.special_tokens.update({\n",
    "            f\"<|reserved_{i}|>\": 128002 + i for i in range(256) if (128002 + i) not in self.special_tokens.values()\n",
    "        })\n",
    "\n",
    "        self.model = tiktoken.Encoding(\n",
    "            name=Path(model_path).name,\n",
    "            pat_str=r\"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+\",\n",
    "            mergeable_ranks=mergeable_ranks,\n",
    "            special_tokens=self.special_tokens\n",
    "        )\n",
    "\n",
    "\n",
    "    def encode(self, text, bos=False, eos=False, allowed_special=set(), disallowed_special=()):\n",
    "        if bos:\n",
    "            tokens = [self.special_tokens[\"<|begin_of_text|>\"]]\n",
    "        else:\n",
    "            tokens = []\n",
    "\n",
    "        tokens += self.model.encode(text, allowed_special=allowed_special, disallowed_special=disallowed_special)\n",
    "\n",
    "        if eos:\n",
    "            tokens.append(self.special_tokens[\"<|end_of_text|>\"])\n",
    "        return tokens\n",
    "\n",
    "    def decode(self, tokens):\n",
    "        return self.model.decode(tokens)\n",
    "\n",
    "\n",
    "class ChatFormat:\n",
    "    def __init__(self, tokenizer):\n",
    "        self.tokenizer = tokenizer\n",
    "\n",
    "    def encode_header(self, message):\n",
    "        tokens = []\n",
    "        tokens.append(self.tokenizer.special_tokens[\"<|start_header_id|>\"])\n",
    "        tokens.extend(self.tokenizer.encode(message[\"role\"], bos=False, eos=False))\n",
    "        tokens.append(self.tokenizer.special_tokens[\"<|end_header_id|>\"])\n",
    "        tokens.extend(self.tokenizer.encode(\"\\n\\n\", bos=False, eos=False))\n",
    "        return tokens\n",
    "\n",
    "    def encode(self, text):\n",
    "        message = {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": text\n",
    "        }\n",
    "\n",
    "        tokens = self.encode_header(message)\n",
    "        tokens.extend(\n",
    "            self.tokenizer.encode(message[\"content\"].strip(), bos=False, eos=False)\n",
    "        )\n",
    "        tokens.append(self.tokenizer.special_tokens[\"<|eot_id|>\"])\n",
    "        return tokens\n",
    "\n",
    "    def decode(self, token_ids):\n",
    "        return self.tokenizer.decode(token_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b771b60c-c198-4b30-bf10-42031197ae86",
   "metadata": {
    "id": "b771b60c-c198-4b30-bf10-42031197ae86"
   },
   "source": [
    "- Please note that Meta AI requires that you accept the Llama 3.2 licensing terms before you can download the files; to do this, you have to create a Hugging Face Hub account and visit the [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) repository to accept the terms\n",
    "- Next, you will need to create an access token; to generate an access token with READ permissions, click on the profile picture in the upper right and click on \"Settings\"\n",
    "\n",
    "\n",
    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gpt-to-llama/settings.webp?1\" width=\"300px\">\n",
    "\n",
    "- Then, create and copy the access token so you can copy & paste it into the next code cell\n",
    "\n",
    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gpt-to-llama/access-token.webp?1\" width=\"600px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e9d96dc8-603a-4cb5-8c3e-4d2ca56862ed",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e9d96dc8-603a-4cb5-8c3e-4d2ca56862ed",
    "outputId": "e6e6dc05-7330-45bc-a9a7-331919155bdd"
   },
   "outputs": [],
   "source": [
    "from huggingface_hub import login\n",
    "\n",
    "login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "986bc1a0-804f-4154-80f8-44cefbee1368",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141,
     "referenced_widgets": [
      "a1608feac06d4687967a3e398f01c489",
      "518fb202e4b44aaba47f07d1a61b6762",
      "672cdc5aea954de3af851c001a667ad3",
      "eebf8874618746b39cf4a21a2728dc7f",
      "5176834aa8784bba9ec21234b87a8948",
      "e2dc407afcd945c798e30597fddfcb3c",
      "0dccd57dcc5c43a588157cef957c07e8",
      "33ca0cdf2c7f41598a381c4ebe6a4ee1",
      "ee44487f58454dacb522b1e084ffb733",
      "d2c41e71a3f441deaed091b620ac5603",
      "3326b6141a1a4eba9f316df528a9b99a"
     ]
    },
    "id": "986bc1a0-804f-4154-80f8-44cefbee1368",
    "outputId": "5dd7334b-4c71-465a-94d2-c3e95b9ddc58"
   },
   "outputs": [],
   "source": [
    "from huggingface_hub import hf_hub_download\n",
    "\n",
    "tokenizer_file_path = hf_hub_download(\n",
    "    repo_id=f\"meta-llama/Llama-3.2-{LLAMA_SIZE_STR}-Instruct\",\n",
    "    filename=\"original/tokenizer.model\",\n",
    "    local_dir=f\"Llama-3.2-{LLAMA_SIZE_STR}-Instruct\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "_gBhxDtU_nxo",
   "metadata": {
    "id": "_gBhxDtU_nxo"
   },
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer(tokenizer_file_path)\n",
    "chat_tokenizer = ChatFormat(tokenizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c172f89f-d301-439f-b809-46169e5f5945",
   "metadata": {
    "id": "c172f89f-d301-439f-b809-46169e5f5945"
   },
   "source": [
    "&nbsp;\n",
    "# 4. Load pretrained weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "75166128-5899-4995-9b88-9672e135650e",
   "metadata": {
    "id": "75166128-5899-4995-9b88-9672e135650e"
   },
   "outputs": [],
   "source": [
    "def assign(left, right, tensor_name=\"unknown\"):\n",
    "    if left.shape != right.shape:\n",
    "        raise ValueError(f\"Shape mismatch in tensor '{tensor_name}'. Left: {left.shape}, Right: {right.shape}\")\n",
    "\n",
    "    if isinstance(right, torch.Tensor):\n",
    "        return torch.nn.Parameter(right.clone().detach())\n",
    "    else:\n",
    "        return torch.nn.Parameter(torch.tensor(right))\n",
    "\n",
    "\n",
    "def load_weights_into_llama(model, param_config, params):\n",
    "    model.tok_emb.weight = assign(model.tok_emb.weight, params[\"model.embed_tokens.weight\"], \"model.embed_tokens.weight\")\n",
    "\n",
    "    for l in range(param_config[\"n_layers\"]):\n",
    "\n",
    "        # Load attention weights\n",
    "        model.trf_blocks[l].att.W_query.weight = assign(\n",
    "            model.trf_blocks[l].att.W_query.weight,\n",
    "            params[f\"model.layers.{l}.self_attn.q_proj.weight\"],\n",
    "            f\"model.layers.{l}.self_attn.q_proj.weight\"\n",
    "        )\n",
    "        model.trf_blocks[l].att.W_key.weight = assign(\n",
    "            model.trf_blocks[l].att.W_key.weight,\n",
    "            params[f\"model.layers.{l}.self_attn.k_proj.weight\"],\n",
    "            f\"model.layers.{l}.self_attn.k_proj.weight\"\n",
    "        )\n",
    "        model.trf_blocks[l].att.W_value.weight = assign(\n",
    "            model.trf_blocks[l].att.W_value.weight,\n",
    "            params[f\"model.layers.{l}.self_attn.v_proj.weight\"],\n",
    "            f\"model.layers.{l}.self_attn.v_proj.weight\"\n",
    "        )\n",
    "        model.trf_blocks[l].att.out_proj.weight = assign(\n",
    "            model.trf_blocks[l].att.out_proj.weight,\n",
    "            params[f\"model.layers.{l}.self_attn.o_proj.weight\"],\n",
    "            f\"model.layers.{l}.self_attn.o_proj.weight\"\n",
    "        )\n",
    "        model.trf_blocks[l].norm1.weight = assign(\n",
    "            model.trf_blocks[l].norm1.weight,\n",
    "            params[f\"model.layers.{l}.input_layernorm.weight\"],\n",
    "            f\"model.layers.{l}.input_layernorm.weight\"\n",
    "        )\n",
    "\n",
    "        # Load FeedForward weights\n",
    "        model.trf_blocks[l].ff.fc1.weight = assign(\n",
    "            model.trf_blocks[l].ff.fc1.weight,\n",
    "            params[f\"model.layers.{l}.mlp.gate_proj.weight\"],\n",
    "            f\"model.layers.{l}.mlp.gate_proj.weight\"\n",
    "        )\n",
    "        model.trf_blocks[l].ff.fc2.weight = assign(\n",
    "            model.trf_blocks[l].ff.fc2.weight,\n",
    "            params[f\"model.layers.{l}.mlp.up_proj.weight\"],\n",
    "            f\"model.layers.{l}.mlp.up_proj.weight\"\n",
    "        )\n",
    "        model.trf_blocks[l].ff.fc3.weight = assign(\n",
    "            model.trf_blocks[l].ff.fc3.weight,\n",
    "            params[f\"model.layers.{l}.mlp.down_proj.weight\"],\n",
    "            f\"model.layers.{l}.mlp.down_proj.weight\"\n",
    "        )\n",
    "        model.trf_blocks[l].norm2.weight = assign(\n",
    "            model.trf_blocks[l].norm2.weight,\n",
    "            params[f\"model.layers.{l}.post_attention_layernorm.weight\"],\n",
    "            f\"model.layers.{l}.post_attention_layernorm.weight\"\n",
    "        )\n",
    "\n",
    "    # Load output layer weights\n",
    "    model.final_norm.weight = assign(model.final_norm.weight, params[\"model.norm.weight\"], \"model.norm.weight\")\n",
    "\n",
    "    if \"lm_head.weight\" in params.keys():\n",
    "        model.out_head.weight = assign(model.out_head.weight, params[\"lm_head.weight\"], \"lm_head.weight\")\n",
    "    else:\n",
    "        model.out_head.weight = assign(model.out_head.weight, params[\"model.embed_tokens.weight\"], \"model.embed_tokens.weight\")\n",
    "        print(\"Model uses weight tying.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "699cb1b8-a67d-49fb-80a6-0dad9d81f392",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17,
     "referenced_widgets": [
      "9881b6995c3f49dc89e6992fd9ab660b",
      "17a3174e65c54476b2e0d1faf8f011ca",
      "1bbf2e62c0754d1593beb4105a7f1ac1",
      "b82112e1dec645d98aa1c1ba64abcb61",
      "271e2bd6a35e4a8b92de8697f7c0be5f",
      "90a79523187446dfa692723b2e5833a7",
      "431ffb83b8c14bf182f0430e07ea6154",
      "a8f1b72a33dd4b548de23fbd95e0da18",
      "25cc36132d384189acfbecc59483134b",
      "bfd06423ad544218968648016e731a46",
      "d029630b63ff44cf807ade428d2eb421"
     ]
    },
    "id": "699cb1b8-a67d-49fb-80a6-0dad9d81f392",
    "outputId": "55b2f28c-142f-4698-9d23-d27456d3ed6d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model uses weight tying.\n"
     ]
    }
   ],
   "source": [
    "from safetensors.torch import load_file\n",
    "\n",
    "\n",
    "if LLAMA_SIZE_STR == \"1B\":\n",
    "    weights_file = hf_hub_download(\n",
    "        repo_id=f\"meta-llama/Llama-3.2-{LLAMA_SIZE_STR}-Instruct\",\n",
    "        filename=\"model.safetensors\",\n",
    "        local_dir=f\"Llama-3.2-{LLAMA_SIZE_STR}-Instruct\"\n",
    "    )\n",
    "    combined_weights = load_file(weights_file)\n",
    "\n",
    "\n",
    "else:\n",
    "    combined_weights = {}\n",
    "    for i in range(1, 3):\n",
    "        weights_file = hf_hub_download(\n",
    "            repo_id=f\"meta-llama/Llama-3.2-{LLAMA_SIZE_STR}-Instruct\",\n",
    "            filename=f\"model-0000{i}-of-00002.safetensors\",\n",
    "            local_dir=f\"Llama-3.2-{LLAMA_SIZE_STR}-Instruct\"\n",
    "        )\n",
    "        current_weights = load_file(weights_file)\n",
    "        combined_weights.update(current_weights)\n",
    "\n",
    "\n",
    "load_weights_into_llama(model, LLAMA32_CONFIG, combined_weights)\n",
    "model.to(device)\n",
    "del combined_weights  # free up memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7f9f7ccc-70cb-41ff-9c25-44336042fc37",
   "metadata": {
    "id": "7f9f7ccc-70cb-41ff-9c25-44336042fc37"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight tying: True\n"
     ]
    }
   ],
   "source": [
    "print(\"Weight tying:\", torch.equal(model.tok_emb.weight, model.out_head.weight))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57d07df1-4401-4792-b549-7c4cc5632323",
   "metadata": {
    "id": "57d07df1-4401-4792-b549-7c4cc5632323"
   },
   "source": [
    "&nbsp;\n",
    "# 5. Generate text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7b8401c6-e244-4cb7-9849-2ba71ce758d5",
   "metadata": {
    "id": "7b8401c6-e244-4cb7-9849-2ba71ce758d5"
   },
   "outputs": [],
   "source": [
    "def text_to_token_ids(text, tokenizer):\n",
    "    encoded = tokenizer.encode(text)\n",
    "    encoded_tensor = torch.tensor(encoded).unsqueeze(0)  # add batch dimension\n",
    "    return encoded_tensor\n",
    "\n",
    "\n",
    "def token_ids_to_text(token_ids, tokenizer):\n",
    "    flat = token_ids.squeeze(0)  # remove batch dimension\n",
    "    return tokenizer.decode(flat.tolist())\n",
    "\n",
    "\n",
    "def generate(model, idx, max_new_tokens, context_size, temperature=0.0, top_k=None, eos_id=None):\n",
    "\n",
    "    # For-loop is the same as before: Get logits, and only focus on last time step\n",
    "    for _ in range(max_new_tokens):\n",
    "        idx_cond = idx[:, -context_size:]\n",
    "        with torch.no_grad():\n",
    "            logits = model(idx_cond)\n",
    "        logits = logits[:, -1, :]\n",
    "\n",
    "        # New: Filter logits with top_k sampling\n",
    "        if top_k is not None:\n",
    "            # Keep only top_k values\n",
    "            top_logits, _ = torch.topk(logits, top_k)\n",
    "            min_val = top_logits[:, -1]\n",
    "            logits = torch.where(logits < min_val, torch.tensor(float('-inf')).to(logits.device), logits)\n",
    "\n",
    "        # New: Apply temperature scaling\n",
    "        if temperature > 0.0:\n",
    "            logits = logits / temperature\n",
    "\n",
    "            # Apply softmax to get probabilities\n",
    "            probs = torch.softmax(logits, dim=-1)  # (batch_size, context_len)\n",
    "\n",
    "            # Sample from the distribution\n",
    "            idx_next = torch.multinomial(probs, num_samples=1)  # (batch_size, 1)\n",
    "\n",
    "        # Otherwise same as before: get idx of the vocab entry with the highest logits value\n",
    "        else:\n",
    "            idx_next = torch.argmax(logits, dim=-1, keepdim=True)  # (batch_size, 1)\n",
    "\n",
    "        if idx_next == eos_id:  # Stop generating early if end-of-sequence token is encountered and eos_id is specified\n",
    "            break\n",
    "\n",
    "        # Same as before: append sampled index to the running sequence\n",
    "        idx = torch.cat((idx, idx_next), dim=1)  # (batch_size, num_tokens+1)\n",
    "\n",
    "    return idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1c7a04fa-6aac-416b-8f63-f1e19227633d",
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    "id": "1c7a04fa-6aac-416b-8f63-f1e19227633d"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 19.49 sec\n",
      "\n",
      "\n",
      "Output text:\n",
      "\n",
      " Llamas are herbivores, which means they primarily eat plants and plant-based foods. Their diet typically consists of:\n",
      "\n",
      "1. Grasses: Llamas love to graze on various types of grasses, including tall grasses and short grasses.\n",
      "2. Hay: Llamas also eat hay, which is a dry, compressed form of grass or other plants.\n",
      "3. Alfalfa: Alfalfa is a legume that is commonly used as a hay substitute in llama feed.\n",
      "4. Other plants: Llamas will also eat other plants, such as clover, dandelions, and wild grasses.\n",
      "\n",
      "It's worth noting that the specific diet of llamas can vary depending on factors such as\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "\n",
    "PROMPT = \"What do llamas eat?\"\n",
    "\n",
    "torch.manual_seed(123)\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "token_ids = generate(\n",
    "    model=model,\n",
    "    idx=text_to_token_ids(PROMPT, chat_tokenizer).to(device),\n",
    "    max_new_tokens=150,\n",
    "    context_size=LLAMA32_CONFIG[\"context_length\"],\n",
    "    top_k=1,\n",
    "    temperature=0.\n",
    ")\n",
    "\n",
    "print(f\"Time: {time.time() - start:.2f} sec\")\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    max_mem_bytes = torch.cuda.max_memory_allocated()\n",
    "    max_mem_gb = max_mem_bytes / (1024 ** 3)\n",
    "    print(f\"Max memory allocated: {max_mem_gb:.2f} GB\")\n",
    "\n",
    "output_text = token_ids_to_text(token_ids, tokenizer)\n",
    "\n",
    "\n",
    "def clean_text(text, header_end=\"assistant<|end_header_id|>\\n\\n\"):\n",
    "    # Find the index of the first occurrence of \"<|end_header_id|>\"\n",
    "    index = text.find(header_end)\n",
    "\n",
    "    if index != -1:\n",
    "        # Return the substring starting after \"<|end_header_id|>\"\n",
    "        return text[index + len(header_end):].strip()  # Strip removes leading/trailing whitespace\n",
    "    else:\n",
    "        # If the token is not found, return the original text\n",
    "        return text\n",
    "\n",
    "print(\"\\n\\nOutput text:\\n\\n\", clean_text(output_text))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "549324d6-5c71-4147-ae21-2e67675faa3d",
   "metadata": {
    "id": "549324d6-5c71-4147-ae21-2e67675faa3d"
   },
   "source": [
    "&nbsp;\n",
    "# What's next?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6edaaae-2de1-406c-8ffa-897cdfa3808c",
   "metadata": {
    "id": "e6edaaae-2de1-406c-8ffa-897cdfa3808c"
   },
   "source": [
    "- The notebook was kept purposefully minimal; if you are interested in additional explanation about the individual components, check out the following two companion notebooks:\n",
    "\n",
    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gpt-to-llama/gpt-and-all-llamas.webp\">\n",
    "\n",
    "  1. [Converting a From-Scratch GPT Architecture to Llama 2](converting-gpt-to-llama2.ipynb)\n",
    "  2. [Converting Llama 2 to Llama 3.2 From Scratch](converting-llama2-to-llama3.ipynb)\n",
    "  \n",
    "- For those interested in a comprehensive guide on building a large language model from scratch and gaining a deeper understanding of its mechanics, you might like my [Build a Large Language Model (From Scratch)](http://mng.bz/orYv)\n",
    "\n",
    "<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>"
   ]
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